Renaming variables
Here is what the data for 2015 looks like: #### {.scrollable }
Rows: 17,711
Columns: 110
$ psu <chr> "015438", "015438", "015438", "015438", "015438", "015438"…
$ finwgt <dbl> 216.7268, 324.9620, 324.9620, 397.1552, 264.8745, 264.8745…
$ stratum <chr> "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "B…
$ Qn1 <dbl> 10, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,…
$ Qn2 <dbl> 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2…
$ Qn3 <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 5, 5…
$ Qn4a <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Qn4b <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn4c <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn4d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn4e <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn40a <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn40b <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn40c <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn40d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn40e <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn40f <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn40g <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn40h <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Qn41a <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn41b <dbl> NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Qn41c <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn41d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn41e <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn41f <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn41g <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn41h <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn41i <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn41j <dbl> 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ Qn42 <dbl> 11, 1, 11, 1, 11, 1, 4, 1, 11, 11, 11, 3, 11, 11, 11, 1, 1…
$ Qn43a <dbl> 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ Qn43b <dbl> NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Qn43c <dbl> NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Qn43d <dbl> NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Qn43e <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn43f <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn43g <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn43h <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn44a <dbl> 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ Qn44b <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn44c <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn44d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn44e <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn44f <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn44g <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn44h <dbl> NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Qn45a <dbl> 1, NA, 1, NA, 1, NA, NA, NA, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1…
$ Qn45b <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn45c <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn45d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn45e <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn45f <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn45g <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn45h <dbl> NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Qn45i <dbl> NA, NA, NA, NA, NA, 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Qn45j <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn45k <dbl> NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Qn46 <dbl> 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, …
$ Qn47 <dbl> 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ Qn48 <dbl> 1, 1, 1, 1, 1, 1, 6, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, …
$ Qn49 <dbl> 1, 1, 1, 1, 1, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
$ Qn5a <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn5b <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn5c <dbl> 1, 1, 1, NA, NA, NA, NA, NA, 1, 1, 1, NA, 1, 1, 1, NA, 1, …
$ Qn5d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn5e <dbl> NA, NA, NA, 1, 1, 1, 1, 1, NA, NA, NA, 1, NA, NA, NA, 1, N…
$ Qn50 <dbl> 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
$ Qn51 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Qn52 <dbl> 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1…
$ Qn53 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ Qn54 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
$ Qn55a <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, …
$ Qn55b <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn55c <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn55d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn55e <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn55f <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn55g <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn55h <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn55i <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn55j <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn55k <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn55l <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ Qn56 <dbl> 3, 4, 4, 3, 4, 3, 2, 4, 3, 4, 2, 4, 4, 4, 4, 4, 3, 4, 4, 3…
$ Qn57 <dbl> 3, 4, 4, 3, 4, 3, 3, 4, 3, 4, 3, 4, 2, 4, 4, 4, 4, 4, 3, 3…
$ Qn58 <dbl> 4, 4, 4, 3, 4, 3, 3, 4, 3, 4, 3, 4, 4, 4, 4, 3, 4, 4, 4, 4…
$ Qn59 <dbl> 3, 1, 4, 2, 4, 3, 2, 3, 3, 4, 2, 4, 2, 4, 4, 2, 4, 3, 2, 2…
$ ECIGT <dbl> 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1…
$ ECIGAR <dbl> 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2…
$ ESLT <dbl> 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ EELCIGT <dbl> 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1…
$ EROLLCIGTS <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2…
$ EFLAVCIGTS <dbl> 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ EBIDIS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ EFLAVCIGAR <dbl> 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2…
$ EHOOKAH <dbl> 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ EPIPE <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ ESNUS <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ EDISSOLV <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CCIGT <dbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CCIGAR <dbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CSLT <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CELCIGT <dbl> 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CROLLCIGTS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CFLAVCIGTS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CBIDIS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CHOOKAH <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CPIPE <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CSNUS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CDISSOLV <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
Currently, it isn’t very clear what most of the variables indicate.
We want to rename variables like Qn1 to something more meaningful like Age.
To do this we will use the rename() function of the dplyr package. You could also use functions in the tidyselect package
nyts_data[["nyts2015"]] <- nyts_data[["nyts2015"]] %>%
dplyr::rename(Age=Qn1,
female=Qn2,
Grade=Qn3,
Not_HL=Qn4a,
HL_Mex=Qn4b,
HL_PR=Qn4c,
HL_Cub=Qn4d,
HL_Other=Qn4e,
Race_AIAN=Qn5a,
Race_Asian=Qn5b,
Race_BAA=Qn5c,
Race_NHOPI=Qn5d,
Race_White=Qn5e) %>%
mutate(Age=Age+8,
Grade=Grade+5,
brand_ecig=NA,
menthol=NA,
clove_spice=NA,
fruit=NA,
chocolate=NA,
alcoholic_drink=NA,
candy_dessert_sweets=NA,
other=NA,
no_use=NA) %>%
dplyr::select(-starts_with("Q"))
Rows: 17,711
Columns: 48
$ psu <chr> "015438", "015438", "015438", "015438", "015438"…
$ finwgt <dbl> 216.7268, 324.9620, 324.9620, 397.1552, 264.8745…
$ stratum <chr> "BR3", "BR3", "BR3", "BR3", "BR3", "BR3", "BR3",…
$ Age <dbl> 18, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, …
$ female <dbl> 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 1, …
$ Grade <dbl> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, …
$ Not_HL <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ HL_Mex <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ HL_PR <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ HL_Cub <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ HL_Other <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Race_AIAN <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Race_Asian <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Race_BAA <dbl> 1, 1, 1, NA, NA, NA, NA, NA, 1, 1, 1, NA, 1, 1, …
$ Race_NHOPI <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Race_White <dbl> NA, NA, NA, 1, 1, 1, 1, 1, NA, NA, NA, 1, NA, NA…
$ ECIGT <dbl> 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, …
$ ECIGAR <dbl> 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ ESLT <dbl> 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, …
$ EELCIGT <dbl> 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, …
$ EROLLCIGTS <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, …
$ EFLAVCIGTS <dbl> 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ EBIDIS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ EFLAVCIGAR <dbl> 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, …
$ EHOOKAH <dbl> 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, …
$ EPIPE <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ ESNUS <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ EDISSOLV <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CCIGT <dbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CCIGAR <dbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CSLT <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CELCIGT <dbl> 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CROLLCIGTS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CFLAVCIGTS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CBIDIS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CHOOKAH <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CPIPE <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CSNUS <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ CDISSOLV <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ brand_ecig <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ menthol <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ clove_spice <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ fruit <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ chocolate <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ alcoholic_drink <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ candy_dessert_sweets <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ other <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ no_use <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
Data summary
| Name |
nyts_data[[“nyts2015”]] |
| Number of rows |
17711 |
| Number of columns |
48 |
| _______________________ |
|
| Column type frequency: |
|
| character |
2 |
| logical |
9 |
| numeric |
37 |
| ________________________ |
|
| Group variables |
None |
Variable type: character
| psu |
0 |
1 |
6 |
6 |
0 |
82 |
0 |
| stratum |
0 |
1 |
3 |
3 |
0 |
16 |
0 |
Variable type: logical
| brand_ecig |
17711 |
0 |
NaN |
: |
| menthol |
17711 |
0 |
NaN |
: |
| clove_spice |
17711 |
0 |
NaN |
: |
| fruit |
17711 |
0 |
NaN |
: |
| chocolate |
17711 |
0 |
NaN |
: |
| alcoholic_drink |
17711 |
0 |
NaN |
: |
| candy_dessert_sweets |
17711 |
0 |
NaN |
: |
| other |
17711 |
0 |
NaN |
: |
| no_use |
17711 |
0 |
NaN |
: |
Variable type: numeric
| finwgt |
0 |
1.00 |
1539.67 |
1199.32 |
31.52 |
814.04 |
1194.16 |
1792.16 |
6084.76 |
▇▅▁▁▁ |
| Age |
69 |
1.00 |
14.53 |
2.04 |
9.00 |
13.00 |
14.00 |
16.00 |
19.00 |
▂▇▇▇▂ |
| female |
131 |
0.99 |
1.49 |
0.50 |
1.00 |
1.00 |
1.00 |
2.00 |
2.00 |
▇▁▁▁▇ |
| Grade |
90 |
0.99 |
8.85 |
1.96 |
6.00 |
7.00 |
9.00 |
11.00 |
13.00 |
▇▅▇▃▃ |
| Not_HL |
5400 |
0.70 |
1.00 |
0.00 |
1.00 |
1.00 |
1.00 |
1.00 |
1.00 |
▁▁▇▁▁ |
| HL_Mex |
14975 |
0.15 |
1.00 |
0.00 |
1.00 |
1.00 |
1.00 |
1.00 |
1.00 |
▁▁▇▁▁ |
| HL_PR |
17175 |
0.03 |
1.00 |
0.00 |
1.00 |
1.00 |
1.00 |
1.00 |
1.00 |
▁▁▇▁▁ |
| HL_Cub |
17450 |
0.01 |
1.00 |
0.00 |
1.00 |
1.00 |
1.00 |
1.00 |
1.00 |
▁▁▇▁▁ |
| HL_Other |
16073 |
0.09 |
1.00 |
0.00 |
1.00 |
1.00 |
1.00 |
1.00 |
1.00 |
▁▁▇▁▁ |
| Race_AIAN |
16559 |
0.07 |
1.00 |
0.00 |
1.00 |
1.00 |
1.00 |
1.00 |
1.00 |
▁▁▇▁▁ |
| Race_Asian |
16688 |
0.06 |
1.00 |
0.00 |
1.00 |
1.00 |
1.00 |
1.00 |
1.00 |
▁▁▇▁▁ |
| Race_BAA |
14111 |
0.20 |
1.00 |
0.00 |
1.00 |
1.00 |
1.00 |
1.00 |
1.00 |
▁▁▇▁▁ |
| Race_NHOPI |
17218 |
0.03 |
1.00 |
0.00 |
1.00 |
1.00 |
1.00 |
1.00 |
1.00 |
▁▁▇▁▁ |
| Race_White |
6858 |
0.61 |
1.00 |
0.00 |
1.00 |
1.00 |
1.00 |
1.00 |
1.00 |
▁▁▇▁▁ |
| ECIGT |
317 |
0.98 |
1.79 |
0.41 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▂▁▁▁▇ |
| ECIGAR |
414 |
0.98 |
1.83 |
0.37 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▂▁▁▁▇ |
| ESLT |
358 |
0.98 |
1.92 |
0.27 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| EELCIGT |
311 |
0.98 |
1.73 |
0.44 |
1.00 |
1.00 |
2.00 |
2.00 |
2.00 |
▃▁▁▁▇ |
| EROLLCIGTS |
715 |
0.96 |
1.95 |
0.22 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| EFLAVCIGTS |
715 |
0.96 |
1.95 |
0.22 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| EBIDIS |
715 |
0.96 |
1.99 |
0.12 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| EFLAVCIGAR |
715 |
0.96 |
1.91 |
0.28 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| EHOOKAH |
715 |
0.96 |
1.87 |
0.34 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| EPIPE |
715 |
0.96 |
1.98 |
0.15 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| ESNUS |
715 |
0.96 |
1.98 |
0.15 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| EDISSOLV |
715 |
0.96 |
1.99 |
0.09 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| CCIGT |
364 |
0.98 |
1.94 |
0.24 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| CCIGAR |
543 |
0.97 |
1.94 |
0.23 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| CSLT |
328 |
0.98 |
1.97 |
0.18 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| CELCIGT |
270 |
0.98 |
1.89 |
0.32 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| CROLLCIGTS |
767 |
0.96 |
1.98 |
0.15 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| CFLAVCIGTS |
767 |
0.96 |
1.98 |
0.15 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| CBIDIS |
767 |
0.96 |
1.99 |
0.07 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| CHOOKAH |
767 |
0.96 |
1.95 |
0.22 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| CPIPE |
767 |
0.96 |
1.99 |
0.09 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| CSNUS |
767 |
0.96 |
1.99 |
0.09 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| CDISSOLV |
767 |
0.96 |
2.00 |
0.06 |
1.00 |
2.00 |
2.00 |
2.00 |
2.00 |
▁▁▁▁▇ |
| #### |
|
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#Note about difference between recode and fct_recode
nyts_data[["nyts2015"]] <- nyts_data[["nyts2015"]] %>%
mutate_all(~ replace(., . %in% c("."), NA)) %>%
mutate(Age=as.character(Age),
Grade=as.character(Grade)
) %>%
mutate(Age=recode(Age,
`19` = ">18",
),
female=recode(female,
`1`= FALSE,
`2` = TRUE,
.default = NA,
.missing = NA),
Grade=recode(Grade,
`13` = "Ungraded/Other"),
Not_HL=recode(Not_HL,
`1` = TRUE,
.default = FALSE,
.missing = FALSE)) %>%
mutate_at(vars(starts_with("HL", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(starts_with("Race", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(starts_with("E", ignore.case = FALSE),
starts_with("C", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
`2` = FALSE,
.default = NA,
.missing = NA)))
nyts_data[["nyts2016"]] <- nyts_data[["nyts2016"]] %>%
rename(Age=Q1,
female=Q2,
Grade=Q3,
Not_HL=Q4A,
HL_Mex=Q4B,
HL_PR=Q4C,
HL_Cub=Q4D,
HL_Other=Q4E,
Race_AIAN=Q5A,
Race_Asian=Q5B,
Race_BAA=Q5C,
Race_NHOPI=Q5D,
Race_White=Q5E,
female=Q2,
menthol=Q50A,
clove_spice=Q50B,
fruit=Q50C,
chocolate=Q50D,
alcoholic_drink=Q50E,
candy_dessert_sweets=Q50F,
other=Q50G,
no_use=Q50H) %>%
mutate(Age = as.numeric(Age) + 8,
Grade = as.numeric(Grade) + 5,
brand_ecig=NA) %>%
dplyr::select(-starts_with("Q"))
nyts_data[["nyts2016"]] <- nyts_data[["nyts2016"]] %>%
mutate_all(~ replace(., . %in% c("*", "**"), NA)) %>%
mutate(Age=as.character(Age),
Grade=as.character(Grade)
) %>%
mutate(Age=recode(Age,
`19` = ">18",
),
female=recode(female,
`1`= FALSE,
`2` = TRUE,
.default = NA,
.missing = NA),
Grade=recode(Grade,
`13` = "Ungraded/Other"),
Not_HL=recode(Not_HL,
`1` = TRUE,
.default = FALSE,
.missing = FALSE)) %>%
mutate_at(vars(starts_with("HL", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(starts_with("Race", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(starts_with("E", ignore.case = FALSE),
starts_with("C", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
`2` = FALSE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(menthol:no_use),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE)))
nyts_data[["nyts2017"]] <- nyts_data[["nyts2017"]] %>%
rename(Age=Q1,
female=Q2,
Grade=Q3,
Not_HL=Q4A,
HL_Mex=Q4B,
HL_PR=Q4C,
HL_Cub=Q4D,
HL_Other=Q4E,
Race_AIAN=Q5A,
Race_Asian=Q5B,
Race_BAA=Q5C,
Race_NHOPI=Q5D,
Race_White=Q5E,
female=Q2,
menthol=Q50A,
clove_spice=Q50B,
fruit=Q50C,
chocolate=Q50D,
alcoholic_drink=Q50E,
candy_dessert_sweets=Q50F,
other=Q50G,
no_use=Q50H) %>%
mutate(Age = as.numeric(Age) + 8,
Grade = as.numeric(Grade) + 5,
brand_ecig=NA) %>%
dplyr::select(-starts_with("Q"))
nyts_data[["nyts2017"]] <- nyts_data[["nyts2017"]] %>%
mutate_all(~ replace(., . %in% c("*", "**"), NA)) %>%
mutate(Age=as.character(Age),
Grade=as.character(Grade)
) %>%
mutate(Age=recode(Age,
`19` = ">18",
),
female=recode(female,
`1`= FALSE,
`2` = TRUE,
.default = NA,
.missing = NA),
Grade=recode(Grade,
`13` = "Ungraded/Other"),
Not_HL=recode(Not_HL,
`1` = TRUE,
.default = FALSE,
.missing = FALSE)) %>%
mutate_at(vars(starts_with("HL", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(starts_with("Race", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(starts_with("E", ignore.case = FALSE),
starts_with("C", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
`2` = FALSE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(menthol:no_use),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE)))
nyts_data[["nyts2018"]] <- nyts_data[["nyts2018"]] %>%
rename(Age=Q1,
female=Q2,
Grade=Q3,
Not_HL=Q4A,
HL_Mex=Q4B,
HL_PR=Q4C,
HL_Cub=Q4D,
HL_Other=Q4E,
Race_AIAN=Q5A,
Race_Asian=Q5B,
Race_BAA=Q5C,
Race_NHOPI=Q5D,
Race_White=Q5E,
female=Q2,
menthol=Q50A,
clove_spice=Q50B,
fruit=Q50C,
chocolate=Q50D,
alcoholic_drink=Q50E,
candy_dessert_sweets=Q50F,
other=Q50G,
no_use=Q50H) %>%
mutate(Age = as.numeric(Age) + 8,
Grade = as.numeric(Grade) + 5,
brand_ecig=NA) %>%
dplyr::select(-starts_with("Q"))
nyts_data[["nyts2018"]] <- nyts_data[["nyts2018"]] %>%
mutate_all(~ replace(., . %in% c("*", "**"), NA)) %>%
mutate(Age=as.character(Age),
Grade=as.character(Grade)
) %>%
mutate(Age=recode(Age,
`19` = ">18",
),
female=recode(female,
`1`= FALSE,
`2` = TRUE,
.default = NA,
.missing = NA),
Grade=recode(Grade,
`13` = "Ungraded/Other"),
Not_HL=recode(Not_HL,
`1` = TRUE,
.default = FALSE,
.missing = FALSE)) %>%
mutate_at(vars(starts_with("HL", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(starts_with("Race", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(starts_with("E", ignore.case = FALSE),
starts_with("C", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
`2` = FALSE,
.missing = NA))) %>%
mutate_at(vars(menthol:no_use),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE)))
nyts_data[["nyts2019"]] <- nyts_data[["nyts2019"]] %>%
rename(brand_ecig=Q40,
Age=Q1,
female=Q2,
Grade=Q3,
Not_HL=Q4A,
HL_Mex=Q4B,
HL_PR=Q4C,
HL_Cub=Q4D,
HL_Other=Q4E,
Race_AIAN=Q5A,
Race_Asian=Q5B,
Race_BAA=Q5C,
Race_NHOPI=Q5D,
Race_White=Q5E,
female=Q2,
menthol=Q62A,
clove_spice=Q62B,
fruit=Q62C,
chocolate=Q62D,
alcoholic_drink=Q62E,
candy_dessert_sweets=Q62F,
other=Q62G) %>%
mutate(Age = as.numeric(Age) + 8,
Grade = as.numeric(Grade) + 5,
no_use="missing") %>%
dplyr::select(-starts_with("Q"))
nyts_data[["nyts2019"]] <- nyts_data[["nyts2019"]] %>%
mutate_all(~ replace(., . %in% c(".N",".S",".Z"), NA)) %>%
mutate(Age=as.character(Age),
Grade=as.character(Grade)
) %>%
mutate(psu=as.character(psu),
Age=recode(Age,
`19` = ">18",
),
female=recode(female,
`1`= FALSE,
`2` = TRUE,
.default = NA),
Grade=recode(Grade,
`13` = "Ungraded/Other"),
Not_HL=recode(Not_HL,
`1` = TRUE,
.default = FALSE,
.missing = FALSE)) %>%
mutate_at(vars(starts_with("HL", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(starts_with("Race", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing = FALSE))) %>%
mutate_at(vars(starts_with("E", ignore.case = FALSE),
starts_with("C", ignore.case = FALSE)),
list(~recode(.,
`1` = TRUE,
`2` = FALSE,
.default = NA))) %>%
mutate(brand_ecig = recode(brand_ecig,
`1` = "Other", #levels 1,8 combined to `Other`
`2` = "Blu",
`3` = "JUUL",
`4` = "Logic",
`5` = "MarkTen",
`6` = "NJOY",
`7` = "Vuse",
`8` = "Other")) %>%
mutate_at(vars(menthol:no_use),
list(~recode(.,
`1` = TRUE,
.default = FALSE,
.missing =FALSE))) #Ask Michael about this if unclear
Reminder: Current users are a subset of ever users.
We will also use the %>% pipe which can be used to define the input for later sequential steps. This will make more sense when we have multiple sequential steps using the same data object. To use the pipe notation we need to install and load dplyr as well.